import argparse
import datetime
import json
import os
import random
import time
from pathlib import Path

import numpy as np
import ruamel.yaml as yaml
import torch
import torch.backends.cudnn as cudnn
import torch.distributed as dist

import utils
from data import create_dataset, create_sampler, create_loader
from data.utils import save_result
from data.vqa_dataset import vqa_collate_fn
from models.blip_vqa import blip_vqa
from utils import cosine_lr_schedule


def train(model, data_loader, optimizer, epoch, device):
    # train
    model.train()

    metric_logger = utils.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    metric_logger.add_meter('loss', utils.SmoothedValue(window_size=1, fmt='{value:.4f}'))

    header = 'Train Epoch: [{}]'.format(epoch)
    print_freq = 50

    for i, (image, question, answer, weights, n) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
        image, weights = image.to(device, non_blocking=True), weights.to(device, non_blocking=True)

        loss = model(image, question, answer, train=True, n=n, weights=weights)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        metric_logger.update(loss=loss.item())
        metric_logger.update(lr=optimizer.param_groups[0]["lr"])

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger.global_avg())
    return {k: "{:.3f}".format(meter.global_avg) for k, meter in metric_logger.meters.items()}


@torch.no_grad()
def evaluation(model, data_loader, device, config):
    # test
    model.eval()

    metric_logger = utils.MetricLogger(delimiter="  ")
    header = 'Generate VQA test result:'
    print_freq = 50

    result = []

    if config['inference'] == 'rank':
        answer_list = data_loader.dataset.answer_list
        answer_candidates = model.tokenizer(answer_list, padding='longest', return_tensors='pt').to(device)
        answer_candidates.input_ids[:, 0] = model.tokenizer.bos_token_id

    for n, (image, question, question_id) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):
        image = image.to(device, non_blocking=True)

        if config['inference'] == 'generate':
            answers = model(image, question, train=False, inference='generate')

            for answer, ques_id in zip(answers, question_id):
                ques_id = int(ques_id.item())
                result.append({"question_id": ques_id, "answer": answer})

        elif config['inference'] == 'rank':
            answer_ids = model(image, question, answer_candidates, train=False, inference='rank',
                               k_test=config['k_test'])

            for ques_id, answer_id in zip(question_id, answer_ids):
                result.append({"question_id": int(ques_id.item()), "answer": answer_list[answer_id]})

    return result


def main(args, config):
    utils.init_distributed_mode(args)

    device = torch.device(args.device)

    # fix the seed for reproducibility
    seed = args.seed + utils.get_rank()
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    cudnn.benchmark = True

    #### Dataset #### 
    print("Creating vqa datasets")
    datasets = create_dataset('vqa', config)

    if args.distributed:
        num_tasks = utils.get_world_size()
        global_rank = utils.get_rank()
        samplers = create_sampler(datasets, [True, False], num_tasks, global_rank)
    else:
        samplers = [None, None]

    train_loader, test_loader = create_loader(datasets, samplers,
                                              batch_size=[config['batch_size_train'], config['batch_size_test']],
                                              num_workers=[4, 4], is_trains=[True, False],
                                              collate_fns=[vqa_collate_fn, None])
    #### Model #### 
    print("Creating model")
    model = blip_vqa(pretrained=config['pretrained'], image_size=config['image_size'],
                     vit=config['vit'], vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'])

    model = model.to(device)

    model_without_ddp = model
    if args.distributed:
        model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
        model_without_ddp = model.module

    optimizer = torch.optim.AdamW(params=model.parameters(), lr=config['init_lr'], weight_decay=config['weight_decay'])

    best = 0
    best_epoch = 0

    print("Start training")
    start_time = time.time()
    for epoch in range(0, config['max_epoch']):
        if not args.evaluate:
            if args.distributed:
                train_loader.sampler.set_epoch(epoch)

            cosine_lr_schedule(optimizer, epoch, config['max_epoch'], config['init_lr'], config['min_lr'])

            train_stats = train(model, train_loader, optimizer, epoch, device)

        else:
            break

        if utils.is_main_process():
            log_stats = {**{f'train_{k}': v for k, v in train_stats.items()},
                         'epoch': epoch,
                         }
            with open(os.path.join(args.output_dir, "log.txt"), "a") as f:
                f.write(json.dumps(log_stats) + "\n")

            save_obj = {
                'model': model_without_ddp.state_dict(),
                'optimizer': optimizer.state_dict(),
                'config': config,
                'epoch': epoch,
            }
            torch.save(save_obj, os.path.join(args.output_dir, 'checkpoint_%02d.pth' % epoch))

        dist.barrier()

    vqa_result = evaluation(model_without_ddp, test_loader, device, config)
    result_file = save_result(vqa_result, args.result_dir, 'vqa_result')

    total_time = time.time() - start_time
    total_time_str = str(datetime.timedelta(seconds=int(total_time)))
    print('Training time {}'.format(total_time_str))


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--config', default='./configs/vqa.yaml')
    parser.add_argument('--output_dir', default='output/VQA')
    parser.add_argument('--evaluate', action='store_true')
    parser.add_argument('--device', default='cuda')
    parser.add_argument('--seed', default=42, type=int)
    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')
    parser.add_argument('--dist_url', default='env://', help='url used to set up distributed training')
    parser.add_argument('--distributed', default=True, type=bool)
    args = parser.parse_args()

    config = yaml.load(open(args.config, 'r'), Loader=yaml.Loader)

    args.result_dir = os.path.join(args.output_dir, 'result')

    Path(args.output_dir).mkdir(parents=True, exist_ok=True)
    Path(args.result_dir).mkdir(parents=True, exist_ok=True)

    yaml.dump(config, open(os.path.join(args.output_dir, 'config.yaml'), 'w'))

    main(args, config)
